Abstract
We propose a hybrid tracking algorithm consisting of two trackers built on grayscale appearance models. In a first tracker we employ an object template that consists of several grayscale image patches. Every patch votes for the possible positions of the object undergoing tracking. A grayscale appearance model that is learned on-line is used in a supplementing tracker. A particle swarm optimization algorithm is utilized to shift particles toward more promising regions in the probability density function. Experimental results show that the hybrid tracker outperforms each of the trackers.
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References
de Castro, L.N.: Fundamentals of Natural Computing: An Overview. Physics of Life Reviews 4(1), 1–36 (2007)
Zeki, S.: Localization and Globalization in Conscious Vision. Annual Review Neuroscience 24, 57–86 (2001)
Ommer, B., Buhmann, J.M.: Learning Compositional Categorization Models. In: European Conf. on Computer Vision, vol. III, pp. 316–329 (2006)
van der Merve, R., de Freitas, N., Doucet, A., Wan, E.: The Unscented Particle Filter. Advances in Neural Information Processing Systems 13, 584–590 (2001)
Schmidt, J., Fritsch, J., Kwolek, B.: Kernel Particle Filter for Real-Time 3D Body Tracking in Monocular Color Images. In: IEEE Int. Conf. on Face and Gesture Rec., pp. 567–572 (2006)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. on Neural Networks, pp. 1942–1948. IEEE Press, Piscataway (1995)
Jepson, A.D., Fleet, D.J., El-Maraghi, T.: Robust Online Appearance Models for Visual Tracking. IEEE Trans. on PAMI 25(10), 1296–1311 (2003)
Kwolek, B.: Learning-Based Object Tracking Using Boosted Features and Appearance Adaptive Models. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2007. LNCS, vol. 4678, pp. 144–155. Springer, Heidelberg (2007)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-Based Probabilistic Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Fieguth, P., Terzopoulos, D.: Color-Based Tracking of Heads and Other Mobile Objects at Video Frame Rates. In: Proc. IEEE Conf. on Comp. Vision and Patt. Rec., pp. 21–27 (1997)
Porikli, F.: Integral Histogram: A Fast Way to Extract Histogram in Cartesian Spaces. In: IEEE Computer Society Conf. on Pattern Rec. and Computer Vision, pp. 829–836 (2005)
Levin, A., Viola, P., Freund, Y.: Unsupervised Improvement of Visual Detectors Using Co-Training. In: Proc. Int. Conf. on Comp. Vision, pp. 626–633 (2004)
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Kwolek, B. (2008). Object Tracking Using Grayscale Appearance Models and Swarm Based Particle Filter. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_54
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DOI: https://doi.org/10.1007/978-3-540-87656-4_54
Publisher Name: Springer, Berlin, Heidelberg
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